Recap: My ultimate goal, for some reason, is to see if you can use genetic algorithms to “train” a computer to play Super Mario Bros. A genetic algorithm needs two key things: a way to generate candidate solutions (relatively easy, here) and a way to measure their “fitness” (or “goodness”) so as to decide which candidates live on to the next generation and which go the way of the dodo.

Of course, being a master of computer science, I won’t be satisfied by creating something that is only capable of playing Super Mario Bros. Nay, I want something that can, in principle, be applied to any Nintendo game. Naturally, a fitness function appropriate for Super Mario Bros. (whatever that might be) would be quite different than one appropriate for, say, Mega Man 2, to say nothing of something like StarTropics or (dare I say it) Final Fantasy.

Clearly, the user of whatever program I create will need some way to specify the desired fitness function. This must be powerful enough to allow the user to make genuinely useful fitness functions, as well as providing enough flexibility to apply to a wide variety of games. And, of course, it needs to be simple enough so that the user stands a chance of using the blasted thing without giving up.

Any fitness function must have some way of measuring a candidate solution. Well, here’s a first stab at it.

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The basic paradigm here is a combination of three simple concepts: metrics, events, and actions.

A metric is a named value you can modify. In principle, a metric measures one particular aspect of a candidate solution. If you know anything about programming, a metric is pretty much just a variable. A fitness function would ultimately be defined as a mathematical combination of one or more metrics.

An event is a condition you can associate one or more actions with. After each video frame, all events are checked to determine whether or not they fire. If they do, their associated actions are executed.

An action specifies how to modify one of the metrics when the action’s event occurs.

An example should make it clear how this is supposed to work. In the example above, we’re trying to measure the wall clock time that passes while playing. We’ve defined two metrics: Minutes and Seconds. Knowing that for an NTSC NES game there are about 60 video frames per second (closer to 60.1, technically), we can say that after every 60 frames, we should increment Seconds by one. Similarly, after every 3600 video frames, a minute has passed, so we increment Minutes by one and subtract 60 from Seconds.

In practice, these measurements wouldn’t be very helpful. If we wanted to measure how long a run took, we’d more likely just want to count the frames and be done with it. But since at this point I haven’t yet implemented more interesting events and actions (which will involve screen scraping in one way or another), this is about all the code can do at the moment.

Nevertheless, it does serve as a proof of concept for how this will work. I suspect this will prove to be a good balance among power, flexibility, and ease of use.

And since you’re all dying to know, here’s how long it took me to play through Air Man’s stage. (Yes, on Difficult; I’m not a wimp.)

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Remember that that’s me playing, not any fancy randomly-generated, natually-selected solution. My code isn’t capable of doing any of that.